• Mozambique is among the 10 countries with the greatest malaria burden.
• Prevalence as high as 40% in some regions.
• 45% of all outpatient cases.
• 56% of all inpatient cases.
• 26% of all hospital deaths.

• 100 deaths daily.

• Bordering countries in elimination phase.

1. Impact on malaria incidence (burden)

2. Impact on labour force (absenteeism and productivity)

3. Impact on school age children (absenteeism and performance)

• Early childhood health shocks -> long-term economic outcomes (Smith 2009, Case et al 2002 & 2005, Currie and Stabile 2003)
• Malaria shown to have lifelong effects on economic outcomes in children - poor cognitive ability, higher absenteeism, poor labour market outcomes (Sachs and Malaney 2002, Lucas 2010)
• Malaria infections among adults (Cutler 2010) shown to lower household income, worsen nutrition and schooling, restrict labour opportunities

We're different

• Previous studies are ecological, retrospective, long-term
• Ours is person-level, real-time, short-term

Potential impact

• Elimination campaigns: very easy to estimate cost; very hard to estimate benefit
• Short-term trumps the long-term for most policy-makers

• We compare grades of students from selected schools in Intervention (Magude district) and Control (Manhiça district) areas
• For both intervention and control we collected data for the year before the intervention started (year 2015) and for the year after the intervention started (year 2016).
• We apply difference in difference analysis to identify and measure the short term impact of the elimination campaign

In diff-diff models: assumption: parallel trends (in the outcome variable) in treatment and control groups before the introduction of the intervention In order to check this crucial assumption…

The coefficients for the interaction between trimester and intervention area identify the differential trends in the outcome variable over time between the treated and control regions. Thus, as the policy is implemented in 2016, these coefficients identify any differential pre-trends in the outcome variable between treated and control regions (the crucial assumption for diff-diff models).

• 4 Schools in Magude (Intervention); 5 Schools in Manhiça (Control)
• Pre-intervention: year 2015
• Post-during intervention: year 2016

• 9 schools (5 intervention, 4 control)
• 140 classes (across the 2 years observation)
• 8,832 students
• 222,503 observations (on trimester examination)
• 860,927 observations (on daily absenteeism) (unbalanced panel)

All regressions controlling for school. Regressions II and III also controlling for subject.

**The impact of the policy is to increase the probability of passing the exam by 2 percentage points. Given that the proportion of those passing examinations was 87.5% in the intervention area in 2015, the increase due to the intervention is (0.02/0.875*100) 2.28%.**

The impact of the policy is to increase the probability of passing the exam by 5 percentage points for the case of maths. Given that the proportion of those passing examinations was 76.69% in the intervention area in 2015, the increase due to the intervention is (0.05/0.7669*100) 6.52%.

OLS regression; regression controlling for school (coeff not shown);

The impact of the policy is to increase the grade for all subjects by 0.23 percentage points. Given that the mean grade was 12.11 in the intervention area in 2015, the increase due to the intervention is (0.22/12.11*100) 1.9%.

• Preliminary results point to a positive short term impact of the malaria campaign on school performance, both in terms of grades as continutous variable and passing examinations;
• The channel through which the campaign had an impact on school performance is absenteeism reduction, the next outcome to investigate;
• Next steps:
• controlling for students’ characteristics: sex, socio-economic conditions, etc
• Investigating issues related to the multilevel nature of database
• Investigating issues related to unbalanced panels

Showing the difference between 2015 and 2016 average grades

Call:
lm(formula = value ~ district + factor(year) + district * factor(year), 
    data = aprop)

Residuals:
     Min       1Q   Median       3Q      Max 
-12.3761  -1.5120  -0.1547   1.6239   7.8453 

Coefficients:
                                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)                      12.15468    0.01810 671.686  < 2e-16 ***
districtManhiça                   0.08903    0.02439   3.650 0.000262 ***
factor(year)2016                  0.35736    0.02394  14.926  < 2e-16 ***
districtManhiça:factor(year)2016 -0.22500    0.03240  -6.945  3.8e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.838 on 126050 degrees of freedom
Multiple R-squared:  0.002106,  Adjusted R-squared:  0.002082 
F-statistic: 88.67 on 3 and 126050 DF,  p-value: < 2.2e-16

Call:
lm(formula = value ~ district + factor(year) + district * factor(year), 
    data = lluny)

Residuals:
     Min       1Q   Median       3Q      Max 
-12.3052  -1.5268  -0.1547   1.6948   7.8453 

Coefficients:
                                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)                      12.15468    0.01727 703.913  < 2e-16 ***
districtManhiça                   0.15053    0.02148   7.007 2.45e-12 ***
factor(year)2016                  0.35736    0.02285  15.642  < 2e-16 ***
districtManhiça:factor(year)2016 -0.13579    0.02921  -4.649 3.33e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.708 on 145989 degrees of freedom
Multiple R-squared:  0.002785,  Adjusted R-squared:  0.002764 
F-statistic: 135.9 on 3 and 145989 DF,  p-value: < 2.2e-16

Call:
lm(formula = value ~ district + factor(year) + district * factor(year), 
    data = lluny)

Residuals:
     Min       1Q   Median       3Q      Max 
-12.3052  -1.5268  -0.3052   1.6948   7.9429 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     12.30521    0.01260 976.907  < 2e-16 ***
districtMagude                  -0.24810    0.04067  -6.101 1.06e-09 ***
factor(year)2016                 0.22156    0.01793  12.357  < 2e-16 ***
districtMagude:factor(year)2016  0.14388    0.05175   2.780  0.00543 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.669 on 101644 degrees of freedom
Multiple R-squared:  0.002302,  Adjusted R-squared:  0.002272 
F-statistic: 78.17 on 3 and 101644 DF,  p-value: < 2.2e-16

• 9 schools
• 7626 students
• 214675 observations (subject-specific trimester exams)
• Unbalanced panel

2985 of 4958 (60.21%)

(One observation = 1 trimester-class)

(One observation = 1 trimester-class)

Error in -n: invalid argument to unary operator

Error in -n: invalid argument to unary operator

Error in -n: invalid argument to unary operator

of 4510 (%)

(One observation = 1 student-day)

(One observation = 1 student-day)

Error in summarise_impl(.data, dots): invalid 'type' (closure) of argument

• 596685 student-days observed (absenteeism)

• 214675 student-class-trimesters observed (performance)